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Automating Scoliosis Analysis By Amar Sahai Thomas Jefferson High School for Science and Technology 2008-2009.

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Presentation on theme: "Automating Scoliosis Analysis By Amar Sahai Thomas Jefferson High School for Science and Technology 2008-2009."— Presentation transcript:

1 Automating Scoliosis Analysis By Amar Sahai Thomas Jefferson High School for Science and Technology 2008-2009

2 Purpose Scoliosis = curvature of the spine Current analyses are either expensive or manual and time-consuming Try to automate this Saves time, effort and money

3 Scope of Study Automating scoliosis detection and angle of curvature Help pinpoint places on spine to apply pressure to most effectively deal with curve Simpler and cheaper than other solutions

4 Similar Studies Detection and Measurement of Hilar Region in Chest Radiograph – Australia, 2003 Automatic Computer Recognition and Analysis of Dental X-ray Film – New York, 1970

5 Procedures and Methodology – Phase 1 Coded in Java and C Requires x-ray images as input Converts inputted images to.pgm image format Uses edge detection to get a clean outline of the spine Darkens image to reduce noise

6 Procedures and Methodology – Phase 2 Convert.pgm file output from Phase 1 to a.gif Input.gif into new Java program that allows user input Program displays.gif as background Program accepts mouse as input device Left click draws point at clicked location Program draws a line between every other point and calculates the acute angle from the vertical

7 Procedures and Methodology – Phase 2 (cont.) Right-click toggles auto-detect mode Left click in this mode draws a point at the “edgiest” spot within a 10 pixel radius Program draws a line between every other point and calculates the acute angle from the vertical Points and lines are color coded  Points: red in normal, green in auto-detect  Lines: blue in normal, green in auto-detect

8 Edge Detection Previous algorithm was fairly primitive Checked for brightest point Current new method – Sobel Calculates horizontal & vertical gradients

9 Edge Detection (cont.) Gradients are determined by “masks”  Horizontal -1 0 1 -2 0 2 -1 0 1  Vertical 1 2 1 0 0 0 -1 -2 -1 Each set of pixels is multiplied by each mask Any pixel with a high enough G value is recorded as an edge

10 Roadblocks File conversion must be done in another program Must erase ribcage in order to detect points to apply pressure Need to add buttons to add more features without making

11 Current Results My edge detection quality is improving but still needs work for actual use Must eradicate extraneous parts of image (ribcage, pelvis)‏ User interface phase works in manual and semi-automatic modes

12 Screenshots

13 Screenshots (cont.)


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